Copyright(copyright) 1994 Texas Instruments Incorporated
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This invention is related to a method and apparatus for predicting product defects.
Texas Instruments Incorporated Defense Systems and Electronics Group (DSEG), a 1992 Malcolm Baldrige Quality Award Winner, has developed both the technology and deployment infrastructure for a quality initiative named Six Sigma. Aimed at measuring and reducing variation in a product or service, this technology has allowed many Texas Instruments disciplines, including software development, to quantify progress towards zero defects. Six Sigma originated with a concept that variation in a product or service is the culprit behind defects. Consequently, one can begin with customer requirements and identify units of product or service in which opportunities for defects and actual defects are itemized. Through simple computations, a sigma level of performance may be identified. Higher sigma values represent higher quality levels. Six Sigma performance is equivalent to no more than 3.4 defects per million opportunities. But why do we care about Six Sigma? In terms of cost of quality, a Four Sigma organization cannot compete with a Six Sigma organization A Four Sigma organization will spend as much as 25% of each sales dollar on cost of non-conformance while a Six Sigma organization will spend less than 1%. Organizations performing at Four Sigma will spend 25 times more dollars on rework, etc. than a Six Sigma organization will. Additionally, Six Sigma analysis supports defect causal analysis, prevention and prediction. It has been incorporated into our process improvement program and is part of a widespread training program for all employees of Texas Instruments DSEG. Over the past 5 years, we believe that this technology contributed to our 10X improvement in software fault density. Six Sigma measurement begins with the customers and their requirements. Defect categories may then be identified along with units of product or service. From this framework, defects per unit, opportunities for defects, defects per million opportunities and sigma levels may be computed. Defects per million opportunities (DPMO) may be computed by simply dividing the defects per unit (DPU) by the opportunity count (source lines of delivered software) and then multiplying by 1 million.
The number of defects per million opportunities (DPMO) may be converted into a Sigma level via use of a conversion chart as shown in FIG. 1. Note that changes in the DPMO levels are not linearly proportional to the Sigma levels. Thus, for an organization of higher levels of sigma performance, smaller amounts of DPMO improvement are needed to achieve the same sigma improvement as that of an organization at a lower level of sigma performance. Sigma measures support benchmarking of quality levels.
In the development of software there are generally at least five stagesxe2x80x94the first being the requirement stage (RA); the second being the preliminary design stage (PD); the third being the design detail stage (DD); the fourth being the coding stage (CUT), and the fifth being the integrating and testing stage (IandT). Defects in software development activity represent rework, resulting in increased cost and cycle times.
While it is known to make estimates of escaping defects mode during a latter phase of integration and test using Weibull curve fittings, there is no known solution for long range prediction of the number of escaping defects.
In accordance with one preferred embodiment of a present invention, there is provided a method and statistical tool apparatus for predicting defects in products. The method according to one embodiment includes the step of providing historical data of defects at different stages of development and a value representing a goal for escaping defects. Also provided is the planned total number of opportunities for defects. The goal for number of escaping defects and planed number of opportunities for defects are backsolved to determine the total number of defects. The total defects are distributed as a function of the historical data to provide prediction of defects at the different stages of development.
The apparatus according to one preferred embodiment of the present invention comprises a processor, a memory, a keyboard, a drive for loading a software package and a display. The processor is loaded with a program for storing historical data indicating the historical pattern of defect containment in the stages of development. The processor has stored therein algorithms for computing sigma values based on opportunities and escaping defects in the stages and including an algorithm for backsolving from historical data.
These and other features of the invention will be apparent to those skilled in the art from the following detailed description of the invention, taken together with the accompanying drawings.